What you will learn
- Understand the fundamental principles and architecture of MDM
- Design and implement effective MDM data models
- Manage data quality and implement cleansing techniques
- Choose the right MDM implementation strategy based on organizational needs
- Establish MDM governance frameworks and ensure data security
- Integrate MDM with enterprise systems and handle data conflicts
- Gain hands-on experience with popular MDM tools (optional)
- Explore real-time MDM and IoT integration scenarios
- Stay updated on future trends in the dynamic field of Master Data Management.
Beneficial for
- Database Administrators
- Data Stewards and Governance Professionals
- Data Architects and Modelers
- Business Analysts
- Data Quality Analysts
- Information Management Professionals
- Anyone involved in data management, governance, or IT strategy
Course Pre-requisite
- Basic understanding of data management concepts
- Familiarity with database systems and data structures
- Knowledge of data governance principles (beneficial but not mandatory)
- Basic understanding of enterprise IT environments
Course Outline
Understanding the significance of MDM in data governance
Key concepts and principles of MDM
Overview of MDM architecture and components
Designing and implementing master data models
Hierarchies and relationships in master data
Handling different data structures in MDM
Importance of data quality in MDM
Data profiling and cleansing techniques
Implementing data quality rules and standards
Choosing between centralized, decentralized, or hybrid MDM architectures
Evaluating data consolidation and integration methods
Implementing MDM for various data domains (customer, product, vendor, etc.)
Establishing MDM governance frameworks
Role-based access control and security measures
Ensuring compliance with data regulations and policies
Integrating MDM with ERP, CRM, and other enterprise systems
Data synchronization and data sharing strategies
Managing data conflicts and resolution in integrated environments
Best practices for successful MDM implementation
Case studies of successful MDM projects
Learning from industry-specific MDM success stories
Overview of popular MDM tools in the market
Evaluating and selecting the right MDM technology
Hands-on exercises with a selected MDM tool (optional)
Understanding real-time MDM requirements
Integrating MDM with Internet of Things (IoT) data
Addressing challenges in real-time MDM environments
Exploring emerging trends in Master Data Management
The role of artificial intelligence (AI) and machine learning (ML) in MDM
Continuous learning and staying updated in the evolving MDM landscape
